About this role
The team is seeking a Mid-Level AI DevOps Engineer with 4-7 years of experience in DevOps, cloud infrastructure, automation, and production deployment environments. This role will focus on building, maintaining, and improving scalable infrastructure and deployment pipelines for AI and machine learning applications. The ideal candidate will have strong hands-on experience with cloud platforms, CI/CD, Docker, Kubernetes, infrastructure as code, monitoring, and automation, along with a working understanding of AI/ML deployment workflows.
Key Responsibilities:
- Design, deploy, and manage cloud-based infrastructure for AI and software applications.
- Work with cloud platforms such as AWS, Azure, or GCP.
- Build and maintain infrastructure using tools such as Terraform, CloudFormation, and Ansible.
- Support scalable, secure, and reliable environments for production workloads.
- Optimize infrastructure for performance, cost, availability, and operational efficiency.
- Build and maintain CI/CD pipelines for application and AI service deployments.
- Automate build, testing, deployment, and rollback processes.
- Improve deployment reliability and reduce manual operational tasks using tools such as Azure DevOps, GitHub Actions, and Jenkins.
- Deploy and manage containerized applications using Docker and Kubernetes.
- Support deployment and monitoring of AI/ML models in production environments.
- Implement and maintain monitoring, logging, tracing, and alerting systems using tools like Prometheus, Grafana, and ELK Stack.
- Apply DevSecOps practices across infrastructure and deployment pipelines.
Required Qualifications:
- 4-7 years of experience in DevOps, Cloud Engineering, Site Reliability Engineering, Platform Engineering, or Infrastructure Engineering.
- Strong hands-on experience with at least one cloud platform: AWS, Azure, or GCP.
- Experience building and managing CI/CD pipelines.
- Proficient in Go, Python, Java, Ansible, Terraform, Pulumi, Shell Scripting, Bash, or PowerShell.
- Strong experience with Docker and Kubernetes in production environments.
- Familiarity with AI/ML workflows, model deployment, or MLOps concepts.
What we offer:
The team provides a collaborative work environment that encourages innovation and growth. You will have the opportunity to work on cutting-edge AI technologies and be part of a dynamic team that values your contributions.